The search for neural correlates of operant and observational learning requires a combination of two (experimental) conditions that are very difficult to combine: stable recording from high order neurons and free movement of the animal in a rather natural environment. We developed a virtual environment (VE) that simulates a simplified 3D world for honeybees walking stationary on an air-supported spherical treadmill. We show that honeybees perceive the stimuli in the VE as meaningful by transferring learned information from free flight to the virtual world. In search for neural correlates of learning in the VE, mushroom body extrinsic neurons were recorded over days during learning. We found changes in the neural activity specific to the rewarded and unrewarded visual stimuli. Our results suggest an involvement of the mushroom body extrinsic neurons in operant learning in the honeybee (Apis mellifera).

Operation and planning of a power system are constrained by the rating of power lines. Usually, the static line rating is used for system operation and planning. The static line rating defined for an electric grid uses the same conservative weather assumptions for the whole grid regardless of the location of each line or its maximum-allowable conductor temperature. A separate analysis of the weather magnitudes measured in a pilot line shows how favorable air temperature and solar heating compensate for unfavorable wind speed. However, this compensation is limited for high maximumallowable conductor temperatures. As a result, the risk of the static line rating exceeding this maximum temperature is higher for HTLS conductors. An adaptive static line rating is proposed to control the assumed risk. The wind speed assumption for the static rating is reduced for higher maximum-allowable conductor temperature.

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The actual line rating in overhead lines varies with weather conditions. When a utility defines a rating value for the operation of the line, it takes into account both the security and the forecast ratio. The defined rating is secure if it is below the actual rating. A good rating forecast ratio is obtained when the defined rating is close to the actual rating. Therefore, the aim of a line rating forecasting method is to define a rating value that is close but below the actual value. This paper shows the rating measurements carried out in a pilot overhead line and analyses the performance of several line rating forecasting methods comparing the defined values with the actual ones.

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